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Astaxanthin-rich Nannochloropsis oculata mitigates high-fat diet-induced liver dysfunction in zebrafish by modulating lipid metabolism and oxidative stress
Improvements from incorporating machine learning algorithms into near real-time operational post-processing
Accurate segmentation of localized fuel cladding chemical interaction layers in SEM micrographs with deep learning method
Abstract U-Zr metallic fuels are promising fuel candidates for fast reactor applications. Fuel/cladding chemical interaction (FCCI) is a random, localized, complex interdiffusion phenomenon occurring at the fuel cladding interface under irradiation, thinning the cladding wall. This interaction has been recognized as a limiting factor in deploying metal fuels to achieve higher burnup under steady state operations. The post irradiation examination of Experimental Breeder Reactor II and Fast Flux Test Facility fuel pins with different irradiation conditions have been the primary method for investigating FCCI in metal fuels, utilizing various characterization techniques, including scanning electron microscopy (SEM). This study compared several computer vision and deep learning approaches for the automated segmentation of FCCI layers in SEM micrographs. We deployed and compared state-of-the-art deep learning models for the task of FCCI layer segmentation in SEM micrographs. A deep learning based end-to-end method proved its capability to enable rapid and accurate segmentation of FCCI in as-collected SEM micrographs, making it highly suitable for real-time applications to automate data analysis. The average segmentation mAP achieves a high performance of 90.8% for dozens of FCCI layers. Furthermore, the method reported in this study is extendable to segmentation tasks for other materials with similar resolution, texture, and contrast characteristics, paving the way for accelerated and automated analysis in characterization analysis and beyond.
Evidence of neolithic cannibalism among farming communities at El Mirador cave, Sierra de Atapuerca, Spain
Abstract In El Mirador cave in Sierra de Atapuerca, Spain, a unique collection of human remains provides insights into cannibalistic practices from the Neolithic to the Bronze Age. Six Early Bronze Age individuals (4600–4100 cal BP) showing signs of cannibalism were discovered in the early 2000s. Later excavations uncovered older remains with similar cultural modifications. A Bayesian statistical analysis of the radiocarbon dates identified a single earlier event (5709–5573 cal BP) unrelated to the Bronze Age finds. 87Sr/86Sr analysis showed the cannibalised people were of local origin. The episode coincided with the end of the Neolithic occupation, suggesting this was a not common behaviour among the cave inhabitants. Given the age of the cannibalised individuals and environmental conditions, the data does not indicate a response to famine. This study complements and expands upon our understanding of European prehistoric cannibalism. The current findings suggest that cannibalism may be linked to intergroup violence during late prehistoric periods.
Screening and identification of muscle pericyte selective markers
Abstract Pericytes, which share markers with smooth muscle cells (SMCs), are heterogenous cells. Pericytes in the brain and skeletal muscle have different embryonic origins, representing distinct subpopulations. One challenge in the field is that there are no subpopulation-specific pericyte markers. Here, we compared the transcriptomes of muscle pericytes and SMCs, and identified 741 muscle pericyte-enriched genes and 564 muscle SMC-enriched genes. Gene ontology analysis uncovered distinct biological processes and molecular functions in muscle pericytes and SMCs. Interestingly, the Venn diagram revealed only one gene shared by brain and muscle pericytes, suggesting that they are indeed distinct subpopulations with different transcriptional profiles. We further validated that GSN co-localized with PDGFRβ+SMA− cells in small and large blood vessels but not PDGFRβ+SMA+ cells, indicating that GSN predominantly marks pericytes and fibroblasts rather than SMCs in skeletal muscle. Negligible levels of GSN were detected in the brain. These findings indicate that GSN may serve as a selective marker for muscle pericytes.
The tiny mouse lemur could make for a mighty model organism
Quantitative image analysis of the extracellular matrix of esophageal squamous cell carcinoma and high grade dysplasia via two-photon microscopy
Abstract Squamous cell carcinoma (SCC) and high-grade dysplasia (HGD) are two different pathological entities; however, they sometimes share similarities in histological structure depending on the context. Thus, distinguishing between the two may require careful examination by a pathologist and consideration of clinical findings. Unlike previous studies on cancer diagnosis using two-photon microscopy, quantitative analysis or machine learning (ML) algorithms need to be used to determine the subtle structural changes in images and the structural features that are statistically meaningful in cancer development. In this study, we aimed to quantitatively distinguish between SCC and HGD using two-photon microscopy combined with ML. Tissue samples were categorized into two groups: Group 1, primary SCC vs. metachronous HGD (SCC-HGD) and Group 2, primary HGD vs. metachronous HGD (HGD-HGD). We quantitatively analyzed second harmonic generation (SHG) and two-photon fluorescence (TPF) signals from two-photon microscopy imaging of the extracellular matrix (ECM). Gray-level co-occurrence matrix (GLCM) was used to extract the textural features of the tissue images, and support vector machine (SVM), for classification of the tissue images based on their pathologies. The SHG-based classifiers demonstrated 75%, 84.21%, 95%, and 95.65% for Group 1, Group 2, primary SCC vs. primary HGD, and metachronous HGD (Group 1) vs. metachronous HGD (Group 2), respectively. This integrative approach enabled the characterization of different pathological stages and enhances the understanding of the pathogenic factors involved in the progression of esophageal cancer.
NMN improves cardiac function in SIRT3 knockout mice via the SIRT1/PGC-1α pathway
Identification and experimental validation of mitochondrial and endoplasmic reticulum stress related gene in diabetic nephropathy
Enhanced photoconductive response of ZnO thin films with the impact of annealing temperatures on structural and optical properties
Abstract Zinc oxide (ZnO) is a versatile material widely used in optoelectronic devices due to its broad bandgap (3.37 eV), high electron mobility, and significant exciton binding energy (60 meV). In this study, ZnO thin films were fabricated on SiO₂/Si substrates via thermal evaporation, followed by annealing at 400 °C and 600 °C to investigate the effect of thermal treatment on their structural, optical, and photoconductive properties. X-ray diffraction (XRD) analysis confirmed the formation of the hexagonal wurtzite ZnO structure, with improved crystallinity observed at higher annealing temperatures. The photoconductivity of the films demonstrated enhanced response times and self-powered behavior, particularly in the sample annealed at 600 °C. These findings highlight the potential of ZnO thin films for fast-response photodetection applications and show that controlled annealing significantly influences photosensitivity.
Eco-friendly enhancement of optical and structural properties in polyvinyl alcohol films via eggplant peel dye doping
No indications of weight gain associated DNA methylation changes in patients with anorexia nervosa
Abstract Anorexia nervosa (AN) is a mental disorder marked by a significantly low body weight. Differentially methylated CpG sites have been reported to be involved in body weight regulation. Methylation pattern may change during considerable weight gain by in-patient treatment. Consequently, we aimed to (1) replicate the hypomethylation at the NR1H3 gene locus (identified in our previous epigenome-wide association study) in independent study groups of 189 female patients with AN and 67 healthy-lean female controls, and (2) identify regions associated with large weight gain associated DNA methylation changes in three patients with AN through whole-genome bisulfite sequencing in CD14+ cells. In the replication study, no evidence was observed for hypomethylation at the investigated 15 CpG sites of the NR1H3 locus. Relying on two analysis tools (camel, metilene) to identify differentially methylated regions (DMRs), subtle methylation differences concordant between both tools were detected only when the usual threshold of camel was lowered. Then, eight regions were selected exemplarily for technical replication with deep bisulfite sequencing in the same three patients with AN. None of the regions could be confirmed. Summarising, we could not confirm hypomethylation at NR1H3 and could not detect methylation differences in patients with AN between admission and weight gain at discharge.
Recent advances in film dosimetry for quality assurance in microbeam radiation therapy
Pan-cancer landscape of ITGAV and its potential role in gastric cancer
Development of several machine learning based models for determination of small molecule pharmaceutical solubility in binary solvents at different temperatures
PEGylated liposomal metformin overcomes pharmacokinetic barriers to trigger potent mitochondrial disruption and cell cycle arrest in hepatocellular carcinoma
Abstract This study presents a comprehensive experimental and computational evaluation of PEGylated liposomal metformin as a nanocarrier-based therapeutic strategy for hepatocellular carcinoma (HCC). Liposomal formulations were prepared via thin-film hydration, yielding spherical, well-dispersed vesicles with high encapsulation efficiency (> 90%) and a mean hydrodynamic diameter of 177.2 ± 30.2 nm. PEGylation and metformin loading induced significant physicochemical alterations, as confirmed by differential scanning calorimetry and FTIR spectroscopy, reflecting increased bilayer fluidity and headgroup interactions. Cytotoxicity assays revealed a substantial enhancement in antitumor potency: PEGylated liposomal metformin reduced the IC₅₀ against HepG2 cells to 118.76 μg/mL compared to 2392.81 μg/mL for free metformin—representing a > 20-fold improvement. In Vero cells, IC₅₀ values were 137.13 μg/mL and 2113.86 μg/mL, respectively, yielding a selectivity index of 1.15. Apoptosis analysis demonstrated increased early and late apoptotic populations, with PEGylated formulations inducing total apoptosis rates of 20.67% in HepG2 cells. Cell cycle profiling revealed marked G₀/G₁ arrest, with 78.12% accumulation versus 58.21% in untreated controls. DNA fragmentation analysis via comet assay further supported elevated genotoxic effects in cancer cells. Molecular docking and 100 ns molecular dynamics simulations confirmed stable binding of metformin to mitochondrial Complex I and CDK4/cyclin D3, with a total MM-PBSA binding energy of − 27.33 kcal/mol in the CDK4 complex. These findings demonstrate that PEGylated liposomal encapsulation substantially enhances the cytotoxic profile of metformin, supporting its advancement as a targeted nanotherapeutic candidate for HCC.
Network-based approach identifies key genes associated with tumor heterogeneity in HPV positive and negative head and neck cancer patients
Abstract Head and Neck Squamous Cell Carcinoma (HNSCC) is the seventh most prevalent cancer worldwide and is classified as human papillomavirus (HPV) positive or negative. Substantial heterogeneity has been observed in the two groups, posing a significant clinical challenge. In the disease context, global transcriptional changes are likely driven by a few key genes that reflect the disease etiology more accurately compared to differentially expressed genes (DEGs). We implemented our network-based tool PathExt on 501 TCGA-HNSCC samples (64 HPV positive & 437 HPV negative) to characterize central genes in two subtypes, where in subtype 1, HPV-positive samples were considered as cases and negative as controls, and vice versa in subtype 2. We also identified DEGs and performed several analyses on multiple benchmarking datasets to compare the biology of central genes with DEGs. PathExt key genes performed better with respect to DEGs in both subtypes in recapitulating disease etiology. Gene ontology analysis using central genes revealed shared biological processes such as “epithelial cell proliferation” as well as subtype-specific processes (immune- and metabolic-related processes in subtype 1 and peptide-related processes in subtype 2). However, in the case of DEGs, no subtype-specific processes were seen. Additionally, PathExt central genes did better than DEGs on external validation datasets that were specific to HNSCC and included HNSCC-specific cancer driver genes, FDA-approved therapeutic targets, and pan-cancer tumor suppressor genes. Unlike DEGs, central genes exhibit significant expression in various cell types, enrichment for cancer hallmarks, and mutated protein systems. Central gene expression-based machine learning model shows better performance than DEGs in classifying responders/non-responders with 0.74 AUROC. Lastly, the top 10 potential therapeutic targets and drugs were proposed. Overall, we observed PathExt as a complementary approach to DEGs, characterizing common and HNSCC subtype-specific key genes associated with distinct HNSCC molecular subtypes.
Targeting de novo purine biosynthesis for tuberculosis treatment
BIM-driven digital twin for demolition waste management of existing residential buildings
Abstract With the accelerated development of urbanisation, the construction industry has significantly contributed to environmental degradation due to its substantial energy consumption and construction and demolition (C&D) waste generation. By assessing the ecological impact of the construction industry alongside existing demolition waste management practices, this article aims to develop a conceptual framework to optimise building demolition, transportation, and recycling processes. This study integrates a BIM-driven Digital Twin framework into C&D waste management, aiming to maximise economic benefits and advance the sustainable development of construction practices. Specifically, it simulates the demolition process of an existing townhouse in Washington, D.C., using BIM-Navisworks software and employs a digital twin to update demolition data in real-time. This approach optimises the classification and transportation of demolition waste, enhancing efficiency and sustainability. The study validates the proposed conceptual framework for building demolition waste management through case simulation. Additionally, it utilises BIM-Dynamo software to analyse the economic benefits of demolition waste recycling, demonstrating that a high recycling rate can significantly enhance economic outcomes. The proposed framework leverages BIM technology to optimise demolition and recycling processes, providing a valuable reference for selecting demolition waste management strategies for other buildings.